GH-15937: Expose parameter gainslift_bins for DeepLearning, GAM, GL…

…M, and StackedEnsemble (#15938)

* Propagate gainslift_bins

* update docs

* fix test formatting

h2o-algos/src/main/java/hex/ensemble/Metalearner.java

@@ -118,6 +118,7 @@ protected void setCommonParams(P parms) {
     parms._offset_column = _model._parms._offset_column;
     parms._main_model_time_budget_factor = _model._parms._main_model_time_budget_factor;
     parms._custom_metric_func = _model._parms._custom_metric_func;
+    parms._gainslift_bins = _model._parms._gainslift_bins;
   }
 
   protected void setCrossValidationParams(P parms) {

h2o-algos/src/main/java/hex/gam/MetricBuilderGAM.java

@@ -156,6 +156,7 @@ public ModelMetrics makeModelMetrics(Model m, Frame f, Frame adaptedFrame, Frame
         Vec weights = f.vec(gamM._parms._weights_column);
         if (resp != null && fractionalbinomial != _glmf._family) {
           gl = new GainsLift(preds.lastVec(), resp, weights);
+          gl._groups = m._parms._gainslift_bins;
           gl.exec(gamM._output._job);
         }
       }

h2o-algos/src/main/java/hex/glm/GLMMetricBuilder.java

@@ -246,6 +246,7 @@ protected void computeAIC(GLMModel gm) {
           Vec weights = f.vec(m._parms._weights_column);
           if (resp != null && Family.fractionalbinomial != _glmf._family) { // don't calculate for frac binomial
             gl = new GainsLift(preds.lastVec(), resp, weights);
+            gl._groups = m._parms._gainslift_bins;
             gl.exec(m._output._job);
           }
         }

h2o-algos/src/main/java/hex/schemas/DeepLearningV3.java

@@ -103,6 +103,7 @@ public static final class DeepLearningParametersV3 extends ModelParametersSchema
         "export_checkpoints_dir", 
         "auc_type", 
         "custom_metric_func",
+        "gainslift_bins",
     };
 
 

h2o-algos/src/main/java/hex/schemas/GAMV3.java

@@ -83,7 +83,8 @@ public static final class GAMParametersV3 extends ModelParametersSchemaV3<GAMMod
             "scale", // array, smoothing parameter for GAM,
             "keep_gam_cols",
             "store_knot_locations",
-            "auc_type"
+            "auc_type",
+            "gainslift_bins",
     };
 
     @API(help = "Seed for pseudo random number generator (if applicable)", gridable = true)

h2o-algos/src/main/java/hex/schemas/GLMV3.java

@@ -94,7 +94,8 @@ public static final class GLMParametersV3 extends ModelParametersSchemaV3<GLMPar
             "generate_variable_inflation_factors",
             "fix_tweedie_variance_power",
             "dispersion_learning_rate",
-            "influence"
+            "influence",
+            "gainslift_bins",
     };
 
     @API(help = "Seed for pseudo random number generator (if applicable)", gridable = true)

h2o-algos/src/main/java/hex/schemas/StackedEnsembleV99.java

@@ -56,7 +56,8 @@ public static final class StackedEnsembleParametersV99 extends ModelParametersSc
       "score_training_samples",
       "keep_levelone_frame",
       "export_checkpoints_dir", 
-      "auc_type"
+      "auc_type", 
+      "gainslift_bins",
     };
 
     public static class AlgorithmValuesProvider extends EnumValuesProvider<Algorithm> {

h2o-docs/src/product/data-science/algo-params/gainslift_bins.rst

@@ -1,7 +1,7 @@
 ``gainslift_bins``
 ------------------
 
-- Available in: GBM, DRF, Naïve-Bayes, XGBoost
+- Available in: Deeplearning, DRF, GAM, GBM, GLM, Naïve-Bayes, Stacked Ensemble, XGBoost
 - Hyperparameter: no
 
 Description

h2o-py/h2o/estimators/deeplearning.py

@@ -129,6 +129,7 @@ def __init__(self,
                  export_checkpoints_dir=None,  # type: Optional[str]
                  auc_type="auto",  # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
                  custom_metric_func=None,  # type: Optional[str]
+                 gainslift_bins=-1,  # type: int
                  ):
         """
         :param model_id: Destination id for this model; auto-generated if not specified.
@@ -437,6 +438,10 @@ def __init__(self,
         :param custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName`
                Defaults to ``None``.
         :type custom_metric_func: str, optional
+        :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
+               binning.
+               Defaults to ``-1``.
+        :type gainslift_bins: int
         """
         super(H2ODeepLearningEstimator, self).__init__()
         self._parms = {}
@@ -530,6 +535,7 @@ def __init__(self,
         self.export_checkpoints_dir = export_checkpoints_dir
         self.auc_type = auc_type
         self.custom_metric_func = custom_metric_func
+        self.gainslift_bins = gainslift_bins
 
     @property
     def training_frame(self):
@@ -3239,6 +3245,20 @@ def custom_metric_func(self, custom_metric_func):
         assert_is_type(custom_metric_func, None, str)
         self._parms["custom_metric_func"] = custom_metric_func
 
+    @property
+    def gainslift_bins(self):
+        """
+        Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
+
+        Type: ``int``, defaults to ``-1``.
+        """
+        return self._parms.get("gainslift_bins")
+
+    @gainslift_bins.setter
+    def gainslift_bins(self, gainslift_bins):
+        assert_is_type(gainslift_bins, None, int)
+        self._parms["gainslift_bins"] = gainslift_bins
+
 
 
 class H2OAutoEncoderEstimator(H2ODeepLearningEstimator):

h2o-py/h2o/estimators/gam.py

@@ -106,6 +106,7 @@ def __init__(self,
                  keep_gam_cols=False,  # type: bool
                  store_knot_locations=False,  # type: bool
                  auc_type="auto",  # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
+                 gainslift_bins=-1,  # type: int
                  ):
         """
         :param model_id: Destination id for this model; auto-generated if not specified.
@@ -373,6 +374,10 @@ def __init__(self,
         :param auc_type: Set default multinomial AUC type.
                Defaults to ``"auto"``.
         :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
+        :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
+               binning.
+               Defaults to ``-1``.
+        :type gainslift_bins: int
         """
         super(H2OGeneralizedAdditiveEstimator, self).__init__()
         self._parms = {}
@@ -445,6 +450,7 @@ def __init__(self,
         self.keep_gam_cols = keep_gam_cols
         self.store_knot_locations = store_knot_locations
         self.auc_type = auc_type
+        self.gainslift_bins = gainslift_bins
 
     @property
     def training_frame(self):
@@ -1444,6 +1450,20 @@ def auc_type(self, auc_type):
         assert_is_type(auc_type, None, Enum("auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"))
         self._parms["auc_type"] = auc_type
 
+    @property
+    def gainslift_bins(self):
+        """
+        Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
+
+        Type: ``int``, defaults to ``-1``.
+        """
+        return self._parms.get("gainslift_bins")
+
+    @gainslift_bins.setter
+    def gainslift_bins(self, gainslift_bins):
+        assert_is_type(gainslift_bins, None, int)
+        self._parms["gainslift_bins"] = gainslift_bins
+
     Lambda = deprecated_property('Lambda', lambda_)
 
     def _additional_used_columns(self, parms):

h2o-py/h2o/estimators/glm.py

@@ -115,6 +115,7 @@ def __init__(self,
                  fix_tweedie_variance_power=True,  # type: bool
                  dispersion_learning_rate=0.5,  # type: float
                  influence=None,  # type: Optional[Literal["dfbetas"]]
+                 gainslift_bins=-1,  # type: int
                  ):
         """
         :param model_id: Destination id for this model; auto-generated if not specified.
@@ -411,6 +412,10 @@ def __init__(self,
                excluded in the dataset.
                Defaults to ``None``.
         :type influence: Literal["dfbetas"], optional
+        :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
+               binning.
+               Defaults to ``-1``.
+        :type gainslift_bins: int
         """
         super(H2OGeneralizedLinearEstimator, self).__init__()
         self._parms = {}
@@ -491,6 +496,7 @@ def __init__(self,
         self.fix_tweedie_variance_power = fix_tweedie_variance_power
         self.dispersion_learning_rate = dispersion_learning_rate
         self.influence = influence
+        self.gainslift_bins = gainslift_bins
 
     @property
     def training_frame(self):
@@ -2379,6 +2385,20 @@ def influence(self, influence):
         assert_is_type(influence, None, Enum("dfbetas"))
         self._parms["influence"] = influence
 
+    @property
+    def gainslift_bins(self):
+        """
+        Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
+
+        Type: ``int``, defaults to ``-1``.
+        """
+        return self._parms.get("gainslift_bins")
+
+    @gainslift_bins.setter
+    def gainslift_bins(self, gainslift_bins):
+        assert_is_type(gainslift_bins, None, int)
+        self._parms["gainslift_bins"] = gainslift_bins
+
     Lambda = deprecated_property('Lambda', lambda_)
 
     def get_regression_influence_diagnostics(self):

h2o-py/h2o/estimators/stackedensemble.py

@@ -88,6 +88,7 @@ def __init__(self,
                  keep_levelone_frame=False,  # type: bool
                  export_checkpoints_dir=None,  # type: Optional[str]
                  auc_type="auto",  # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
+                 gainslift_bins=-1,  # type: int
                  ):
         """
         :param model_id: Destination id for this model; auto-generated if not specified.
@@ -173,6 +174,10 @@ def __init__(self,
         :param auc_type: Set default multinomial AUC type.
                Defaults to ``"auto"``.
         :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
+        :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
+               binning.
+               Defaults to ``-1``.
+        :type gainslift_bins: int
         """
         super(H2OStackedEnsembleEstimator, self).__init__()
         self._parms = {}
@@ -197,6 +202,7 @@ def __init__(self,
         self.keep_levelone_frame = keep_levelone_frame
         self.export_checkpoints_dir = export_checkpoints_dir
         self.auc_type = auc_type
+        self.gainslift_bins = gainslift_bins
         self._parms["_rest_version"] = 99
 
     @property
@@ -905,6 +911,20 @@ def auc_type(self, auc_type):
         assert_is_type(auc_type, None, Enum("auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"))
         self._parms["auc_type"] = auc_type
 
+    @property
+    def gainslift_bins(self):
+        """
+        Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
+
+        Type: ``int``, defaults to ``-1``.
+        """
+        return self._parms.get("gainslift_bins")
+
+    @gainslift_bins.setter
+    def gainslift_bins(self, gainslift_bins):
+        assert_is_type(gainslift_bins, None, int)
+        self._parms["gainslift_bins"] = gainslift_bins
+
 
     def metalearner(self):
         """Print the metalearner of an H2OStackedEnsembleEstimator.